1. Introduction

Fisheries acoustics is an effective method in research studies designed to estimate fish biomass, fish distribution, and abundance (Simmonds and MacLennan 2005). Acoustic data collection, is precise, saves time, and does not affect the fish habitats (Chu 2011). With acoustics fish biomass data is directly collected on live fish within the aquatic ecosystems. In space and time fish biomass change, Hilborn and Walters (1992) suggested that fish sizes, fish populations, mortalities, fish reproduction, fish movements, and migrations influence fish biomass. Some of these factors are influenced by artificial reefs constructed and installed under water. Hixon and Beets (1989) explained that artificial reefs influence fish biomass, fish distributions, and abundances in water ecosystems. Underwater construction and deployment of artificial reefs expand fish habitats which attract and inhabit fish, become spawning and nursery grounds for fish thus improving the fisheries productivity (Froeschke et al. 2005).

The acoustic surveys were conducted in Chilam Bay located at the coast of East Sea of Korea, and Gijang marine ranching area is within this bay. In this bay, several artificial reefs were deployed to enhance fisheries productivity, which is being monitored. The ongoing monitoring activities in Chilam Bay involve catch data from gill nets, fisheries acoustics, and water parameters sampling. These research activities supported fish biomass estimation, and water environment analysis for comprehensive fisheries information around artificial reefs. Comprehensive seasonal surveys aimed at fish distribution, species composition, and fish biomass to guide on holistic ecosystem management. Holistic ecosystem management focusses on restoring, protecting, and preserving the aquatic organisms and fish habitats (Horodysky et al. 2016). Estimation of fish biomass could explain the impacts of artificial reefs on different fish species and fish populations to guide on proper fisheries resources utilization. The objectives of this study were; (1) to estimate fish distribution, (2) to determine catch composition, and target strength estimation for pelagic species and (3) estimate fish biomass changes in time and space around Chilam Bay using the split beam echosounder.

2. Materials and Methods

Study site

Chilam-Gijang marine ranching area of 840 hectares were partitioned into 11 transects (Fig. 1). Fisheries acoustic data, and the experimental gill nets data were collected from August to November, 2017. The acoustic surveys were conducted with a 120 kHz split beam scientific echosounder (EK60, Simrad, Norway). During data collection, the echosounder was deployed to record data for both daytime and nighttime surveys around Chilam Bay.

Experimental set up

Experimental gill nets and fish traps were set around artificial reefs in August and November, 2017, and the fish samples caught were identified. Total length (TL) or fork length (FL) and the weight of the fish were measured using 100 cm measuring board (0.1 cm resolution) and digital weighing scale (0.1 g resolution), respectively, to estimate target strength (Godlewska et al. 2009; Foote et al. 1986). A current meter (RCM9, Aanderaa, Norway) was lowered to the 5 m depth layer in Chilam Bay and water environmental parameters were recorded at 10- minute interval.

The parameters recorded were: the current direction and speed, water temperature, conductivity, dissolved oxygen, and turbidity. These recorded water environment parameters are important during acoustic survey design, fish acoustic data recording, and also are vital for understanding the biological behaviors of different fish species. The integrated system of the echosounder, a control unit (General Purpose Transceiver: GPT), and a display unit were assembled. Echo integration method was used to calibrate the echosounder while in the field before data collection. The surveys settings were: frequency (120 kHz); transmitted power (200 W); absorption coefficient (0.0374 dB m^-1); 2-way beam angle (dB re 1 Steradian, -20.7); Pulse duration (512 µs); sound speed (1494 m/s); transducer gain (25.4 dB). The electric power from a large quantity Li-ion battery (12 V, 117 Ah) and an electric inverter supporting the system, the GPT, and transducer were assembled in the field. The pole mount method was used. The echo sounding system, except for the acoustic transducer, was deployed on a fishing vessel. The transducer was positioned at 1.5 m below water surface on the port side of the vessel during acoustic data recording.

Data analysis

Sonar Pro Echoview (ver. 3.30.60, Australia) was used for data analysis. The data variables were displayed in angular positioning raw pings T1, Sv raw pings T1, TS (target strength) raw pings T1, position global positioning system (GPS) fixes, and vessel logs. The 2.0 m was used as the transducer depth below the water surface. The edit bottom of -0.20 m from sea bottom, and the -70.0 dB lock color display were used. The time and the distances measured by the GPS receiver between the grids were displayed in 0.1 n.mile², the 50 m depth range of separation for the surveyed area was used. The scattering acoustic characteristics of fish detected in the acoustic beam, the experimental TS-length conversion formulae; <SV>=<TS>+ 10log₁₀(<n>) (Simmonds and MacLennan 2005). The cross-sectional area was expressed in dB units of measurement as used in fisheries acoustics and the acoustic indexes of acoustic intensities within a surveyed. The generated CSV (comma separated value) data were exported as the SV mean, nautical area scattering coefficient (NASC), Lat-M and Lon-M and time for echo integration (EI) Gijang-autumn and EI Gijang-winter. The survey time was used to count the number of fish detections per transect from T1 up to T11 (Fig. 1). The map converter, converted the GPS coordinates of each survey line into transverse mercator (TM) by adding with 100000 on the y-axis, and the CSV data files were imported into surfer software to generate the NASC distribution maps of each survey time. The conversion factor (CF) for the pelagic species identified from the catch, and their weighted mean, ρ (g/m²) were calculated, and 840 hectares of the total surveyed area were obtained using ArcGIS (ver. 10.5, ESRI, U.S.A.). Multiplying the ρ (g/m²) with 840 hectares and with 10000, the biomass (tons) for each surveyed time and seasons were estimated.

Fig. 1. Acoustical survey lines in the marine ranching area of Chilam-Gijang, Korea. The black triangle (▲) and blank triangle (△) indicate the locations of artificial reefs, the solid arrow (↓), the location of the fish finder suspended with an anchor, and T1 to T11, transects

3. Results

During daytime, the distribution of fish schools in autumn and winter seasons were few and scattered near the sea bottom (Fig. 2 and Fig. 3) whereas during nighttime many fish schools were densely aggregated in the pelagic zone during the study seasons. The NASC values for autumn and winter surveys ranged from 1 to 50000 m²/n.mile² and overlapped around transects T4 and T5 during both daytime and nighttime surveys. The rest of the transects from T1 to T11 had NASC in range of 1 to 1000 m²/n.mile² in autumn. The NASC distributions during the nighttime surveys were between 50 to 250 m²/ n.mile² around T6, and between 250 to 1000 m²/n.mile² from T7 to T11 in autumn.

Fig. 2. NASC distributions during (a) autumn daytime, (b) autumn nighttime, (c) winter daytime, and (d) winter nighttime surveys in 2017

Fig. 3. Echogram of the TS values during (a) autumn daytime, (b) winter daytime, (c) autumn nighttime, and (d) winter nighttime surveys in 2017. T1 to T11 denote the number of transect line

The NASC intensities during daytime, nighttime of both autumn and winter across all transects were between 1 to 50000 m²/n.mile² .Comparing the NASC distributions of autumn and winter seasons, the NASC around T4 and T5 were more aggregated in autumn compared to the NASC obtained in winter around these same transects. The NASC intensities for nighttime surveys were higher than those for the daytime across most of the transects.

Table 1 shows NASC distributions in autumn and winter, 2017. The NASC for autumn daytime obtained was higher than the NASC for the winter daytime, and the NASC for nighttime surveys for winter species were higher than those for nighttime of autumn species. Burgos and Horne (2006) reported that the recorded NASC, and the estimated TS of fish indicate fish aggregations and morphological characteristics of target fish species. This can be used to deduce information about fish species insonified by the echosounder and nature of echoes transmitted (Fornshell and Tesei 2013).

Table 1. NASC distribution in autumn and winter, 2017. ni is number of averaging intervals (0.1 m²/n.mile²) on the i_th transect, NASC, mean backscattering area per 0.1 m²/n.mile²

Transects T2 and T8 had highest NASC during daytime and nighttime surveys, respectively, in autumn. In winter surveys, during daytime all transects had lower NASC compared to nighttime. Around transects T10 and T11 during nighttime, the recorded NASC were highest in winter surveys. These corresponded to the number of fish detections for each transect, and also depending on space and time for each survey.

The fish catches from experimental gill nets and fish traps were categorized into pelagic and demersal species depending on the species ecological habitat preferences. The biometrics for the selected pelagic species, were used for TS estimation as formulae 1 and 2. Table 2 shows pelagic fish species used in the calculation of the biomass.

\(\begin{array}{l} \text {Autumn<TS}_{120 \mathrm{kHz}}=20 \log (T L)-72 \\ W=0.0328 T L^{2.7537} \end{array}\)       (1)

\(\begin{array}{l} \text {Winter } T S_{120 \text { kH }}>=20 \log (T L)-72 \\ W=0.0135 T L^{2.9837} \end{array}\)       (2)

The total length of each fish species was used to calculate their respective target strength. TS was estimated through echo integration, this converted echo signals into fish abundance in the surveyed area (Foote 1987; Zwolinski et al. 2009). The average TS for all selected pelagic species were -44.0 dB and -44.4 dB for autumn and winter seasons, respectively. The dominant species in two seasons were Scomber japonicus and Thamnaconus modestus in autumn and winter surveys, respectively. The demersal species in autumn season were: Chelidonichthys spinosus, Platycephalus indicus, Pleuronectes yokohamae, Argyrosomus robustus, Okamejei kenojei, Cynoglossus robustus, and Okaraplagusia japonica. The winter demersal species were: Chelidonichthys spinosus, Pleuronectes yokohamme, Argyrosomus argentatus, Okaraplagusia japonica, Cynoglossus robustus, Hemitripterus americanus, and Palalichthysis olivaceus. The TS values on the Table 2 were used for calculating the echo integration partitioning to estimate the biomass by species.

Table 2. Pelagic fish species sampled in autumn and winter, 2017. TL is total length, BW, mean body weight, TS, target strength

During biomass estimation, the acoustic signals of pelagic species made significant contributions to echo signals detected by the transducer. The estimated biomass in autumn and winter were shown in Table 3. The estimated fish biomass in autumn were 7.7 tons and 26.0 tons during daytime and nighttime surveys, respectively. The estimated fish biomass in winter were; 2.27 tons and 30.97 tons during daytime and nighttime surveys, respectively. Partitioning the total biomass for all pelagic species identified in Chilam Bay through echo integration, Seriola lalandi contributed the highest biomass of 3.47 tons and 11.7 tons during daytime and nighttime, respectively in autumn surveys. In winter, a group of pelagic species contributed highest biomass of 1.01 tons and 13.48 tons during daytime and nighttime surveys, respectively. This group of pelagic species in winter were; Zeaus faber, Sebastes inermis and Truchurus trancurus.

Table 3. The estimated variables of surveyed species in autumn and winter, 2017. EI is echo integration, CF, conversion factor

4. Discussions

Most fish species are active during night hours (Landsman et al. 2011; Stenevik et al. 2015). During nighttime, fish exhibit movements and migrations which increase acoustic fish detections to the recording scientific echo sounder. Kang et al. (2012) reported that fish schoolings and movements increase echo detections to the transducer triggering increase in the estimated biomass. Nighttime fish schools were densely distributed in the pelagic zone, this increase acoustic signals recorded during the nighttime compared to daytime when few fish schools were distributed near the sea bottom. Yang et al. (2016) reported that Seriola lalandi experience breeding and spawning peak periods during spring and summer every year around the coastal areas, the spawned fry contributed to the biomass estimated in the following autumn season. Scomber japonicus exhibit schoolings during movements and migrations, and also other fish species occupy varying water layers (Kang et al. 2016; Villamor et al. 1997). These fish schools influence biomass estimation during time periods and space (Simmonds and MacLennan 1992; Godlewska et al. 2009). This explain time and space biomass changes. The calculated coefficients of variation (CV) in autumn were 14.3% and 27.20% for the daytime and nighttime, respectively. The CV for winter were 14.0% and 28.0% for the daytime and nighttime, respectively. The CV for nighttime were higher than those of daytime, this explain the difference in estimated fish biomass due to schoolings, movements and other behaviors of pelagic fish during nighttime in different seasons. Demersal fish stay in acoustic dead zone of the sea, and some of the pelagic fish stay in the same zone during daytime. These fish acoustic signals are obscured by the high scatterings of the sea bottom to transducer, Mello and Rose (2009) these acoustic signals around dead zone are discarded during data cleaning, and affects the estimated fish biomass (Djemali and Toujani 2010; Zwolinski et al. 2009). The catch composition from gill nets set around different artificial reefs in the Chilam-Gijang marine ranching area, in each season different fish species were identified, which influenced fish biomass. Fish biomass change explain the fisheries productivity and biodiversity interactions within the ecosystem modified by the deployed artificial reefs (Acosta and Robertson 2002). Artificial reefs influence fish distribution, increase abundance and biomass (Santos et al. 2011), by providing the spawning areas, nursery grounds for fish, and fish shelters. Such areas are inhabited by plants and animal which are preys for fish (Bohnsack and Sutherland 1985). In conclusion, artificial reefs restore fish habitats, biodiversity interactions, and enhance the fisheries productivity.

On this study totally 57 individuals (pelagic: 8 species, 28 individuals; demersal: 7 species, 29 individuals) and 35 individuals (pelagic: 8 species, 20 individuals; demersal: 7 species, 15 individuals) were caught in autumn and winter season, respectively, by the experimental gill net and trap. The composition of species in this study was not simple. There was also a little of ambiguity in the classification of the dominant species due to the small sample size of each species and the selectivity of the sampling gears. It was difficult to use a high catch ability fishing gear or several times of sampling in the marine ranching area to obtain the large sample size of the fish. The marine ranching area was focused on the management of the fish resources rather than the catch. So the authors applied representative one TS function of the bony fishes. However it is necessary in the future study that the TS functions database of coastal fishes are established and applied them to the echo integration partitioning to get higher confidential acoustical biomass estimation relatively.

Acknowledgment

I thank Prof. Won-Yo Lee for valuable time during acoustical experiments, to my laboratory mates, Ms. MinA Heo and Ms. Gyeom Heo for all their tireless efforts rendered to make this work a success, to Prof. Hong Sung Yun for reviewing the manuscript.